z.par2cdf {lmomco} | R Documentation

## Cumulative Distribution Function of Blipped Distributions

### Description

This function acts as a front end of dispatcher to the distribution-specific cumulative distribution functions.

x(F) = begin{cases} 0, & 0 <= F <= p \ x_G(frac{F-p}{1-p}), & F > p end{cases}

### Usage

    z.par2cdf(x,p,para,z=0,...)

### Arguments

`x` | A real value.
`p` | Nonexceedance probability of the `z` value. This probability could simply be the portion of record having zero values if `z=0`.
`para` | The parameters from `lmom2par` or similar.
`z` | Threshold value.
`...` | The additional arguments are passed to the cumulative distribution function such as `paracheck=FALSE` for the Generalized Lambda distribution (`cdfgld`).

### Value

Nonexceedance probability (0 <= F <= 1) for `x`.

### Author(s)

W.H. Asquith

### See Also

`z.par2qua`, `par2cdf`

### Examples

    # see the example for z.par2qua for more context
    ## define the real parent (or close)
    the.gpa <- vec2par(c(100,1000,0.1),type='gpa')
    fake.data <- rlmomco(30,the.gpa) # simulate some data
    fake.data <- sort(c(fake.data,0,0,0,0)) # add of zero observations

    # next compute the parameters for the positive data
    gpa <- pargpa(lmoms(fake.data[fake.data > 0]))
    n <- length(fake.data) # sample size
    p <- length(fake.data[fake.data == 0])/n # est. prob of zero value
    F <- nonexceeds() # handy values, to get nice range of x
    x <- z.par2qua(F,p,gpa) # x are now computed

    PP <- pp(fake.data) # compute plotting positions of sim. sample

    plot(PP,fake.data,ylim=c(0,5000)) # plot the sample
    lines(cdfgpa(x,the.gpa),x) # the parent (without zeros)
    lines(z.par2cdf(x,p,gpa),x,lwd=3) # fitted model with zero conditional

    # now repeat the above code over and over again and watch the results

[Package lmomco version 0.92 Index]